This study systematically evaluates a wide range of chunking methods for Retrieval-Augmented Generation (RAG) to assess their effectiveness and highlight the overlooked challenges associated with chun…

This paper introduces SVD-RAG, a cost-efficient and content-adaptive summarization method for hierarchical Retrieval-Augmented Generation systems using Singular Value Decomposition on dense sentence e…

This paper evaluates the effectiveness of cluster-based semantic chunking compared to fixed-size and recursive chunking in Retrieval-Augmented Generation systems using the Retrieval Augmented Generati…

The paper systematically compares multiple content representations for RAG pipelines and finds that answer retention—the ability of the representation to preserve the original answer-bearing content—i…

This paper proposes a multi-turn retrieval-augmented generation pipeline for conversational systems across four domains.

The paper proposes formulating RAG design as an architecture search problem and introduces RAISE, a comprehensive framework and benchmark for systematically optimizing RAG hyperparameters.

The paper proposes InSemRAG, an enhanced RAG framework that improves retrieval accuracy and knowledge integrity by incorporating intent-aware retrieval and semantics-preserving chunking, achieving sta…

This paper provides the first comprehensive, end-to-end survey dedicated to the security of Retrieval-Augmented Generation (RAG) systems, systematically mapping threats, defenses, and benchmarks acros…

This paper introduces AB-RAG, a training-free and backbone-agnostic framework for adaptively generating answers, estimating their confidence, and deciding whether to retrieve more evidence based on th…

This paper proposes a comprehensive taxonomy (SLOT) to systematically categorize security risks, attacks, and defenses specific to Retrieval-Augmented Generation (RAG), clarifying that these risks are…

The paper introduces TechGraphRAG, an advanced, agentic RAG framework that enhances technical literature reasoning by integrating multi-step query refinement, external database searching, and knowledg…

The paper systematically evaluates advanced retrieval-augmented generation (RAG) architectures for Cyber Threat Intelligence (CTI), demonstrating that a hybrid graph-text approach significantly improv…

This paper proposes Tail-Aware Adaptive-k (TAA-k), a training-free framework for adaptive context selection in retrieval-augmented generation systems using Extreme Value Theory.

The paper introduces PlanRAG, a framework for Retrieval-Augmented Generation (RAG) that models exploratory reasoning problems as logical query trees, addressing representation and optimization gaps be…

This paper introduces RAGAL, a retrieval-augmented assistant for technical support teams, built under three constraints: zero data egress, read-only, and limited resources. The highest-leverage invest…

This paper introduces DynaKRAG, a method for multi-hop retrieval-augmented generation that learns a shared policy for evidence operations, achieving state-of-the-art results on three benchmarks.

DS@GT ARC evaluates the effectiveness of Corrective RAG (CRAG) and CiteFix in improving citation faithfulness and answer grounding in Retrieval-Augmented Generation (RAG) QA systems, revealing a trade…

This paper empirically evaluates the use of Retrieval-Augmented Generation (RAG) for malware explanation and finds that RAG frequently degrades explanation quality by adding noise when structured secu…

The paper proposes GroundedCache, an evidence-validated cache router that significantly improves the safety of reusing cached semantic answers in RAG systems by requiring multiple gates to validate th…